A Beginners Guide to Edge Computing

We can define edge computing as a distributed IT architecture that makes it possible to process data on the periphery – as close to the originating source as possible. If all this sounds gibberish, hold on.

The past decade has seen tremendous growth in the number of internet-connected devices, which has given rise to a technology known as the Internet of Things (IoT). Simply put, IoT is just a concept of inter-connecting various devices and connecting each of the devices to the internet with a simple on/off switch. This includes everything from cell phones, coffee makers, fridge, washing machines, wearable devices, and any device you can think of that easily connects to any device and transfers data seamlessly.

As IoT started gaining momentum, a problem arose – that of dealing with the data from these inter-connected devices. There’s no need to remind that the data we’re talking about is terabytes in size. Traditionally, the data collected from these devices was sent to the organisation’s central cloud for processing. However, it was a rather time taking process, owing to the size of the data files. Transferring such large datasets over the network to a central cloud can also expose sensitive organisational data to vulnerabilities.

5 Applications of Natural Language Processing for Businesses in 2018

 

Edge computing came into the picture to tackle all this and more. Now, have a look at the first para again and allow us to walk you through the definition slowly.

MBA to become manager

The name ‘edge computing’ refers to computation around the corner/edge in a network diagram. Edge computing pushes all the significant computational processing power towards the edges of the mesh. Like we said earlier – as close to the originating device as possible.

How does this help?

Consider a smart traffic light. Instead of calling home whenever in need of data analysis, if the device is capable of performing analytics in-house, it can accomplish real-time analysis of streaming data and even communicate with other devices to finish tasks on the go. Edge computing, therefore, speeds up the entire analysis process, enabling quick decision-making.

Edge computing is also beneficial for the organisations as it helps them cut down costs that were earlier incurred on transferring data sets over a network. Other than that, it also allows the organisations to filter out the useful data from the device’s periphery itself – thereby enabling organisations to collect only valuable data and ensuring them to cut down costs on cloud computing and storage. Further, edge computing also reduces the response time to milliseconds, all the while conserving the network resources. Using edge computing, we don’t necessarily need to send the data over a network. Instead, the local edge computing system is responsible for compiling the data and sending frequent reports to the central cloud storage for long-term storage. Clearly, by only sending the essential data, edge computing drastically reduces the data that traverses the network.

The deployment of Edge Computing is ideal in a variety of situations. One such case is when the IoT devices have weak internet connectivity, and it’s not practical for them to be connected to a central cloud constantly.

5 Breakthrough Applications of Machine Learning

 

Other such situation can be when there’s a requirement of latency-sensitive processing of data. Edge computing eliminates the factor of latency as the data does not need to be transferred over a network to central cloud storage for processing. This is ideal for financial or manufacturing services where latencies of milliseconds are challenging to achieve.

One more use case for edge computing has been the development of the next-gen 5G cellular networks. Kelly Quinn, a research manager at IDC and an expert in edge computing, predicts that as telecom providers incorporate 5G into their wireless networks, they will start adding micro-data centres by either integrating into or locating adjacent to the 5G towers. Business customers would be able to own or rent space in these micro-data centres to perform edge computing and have direct access to a gateway into the telecom provider’s central network, which can be connected to a public IaaS cloud provider.

Let’s take a look at some other use cases of Edge Computing:

  • Drones are capable of reaching remote places that human can’t even think of. Edge computing enables these drones to review, analyze, and respond to the analysis in real-time. For instance, if a drone finds any emergency situation, it can instantaneously provide valuable information to people nearby without having first to send the data over a network and then receive the analysis.
  • Augmented Reality– The introduction of edge computing has taken Augmented Reality a step further. An edge computing platform can provide highly localised data targeted at user’s point of interest; thereby enhancing the AR services.
  • Automated vehicles– Giants like Google and Uber are coming up with self-driving cars. Edge computing plays a crucial role in the development of such automatic vehicles. These vehicles can process and transmit vital data in real-time to other vehicles commuting nearby using edge computing. These giants aim to make such self-driving cars a consumer reality by 2020. With the introduction of such automated vehicles, we’re sure to see a decrease in the number of lives lost due to automobile accidents.

Having said all this, there are still some compromises and challenges that can’t be neglected when talking about edge computing. First of all, only a minute subset of the whole data is processed and analyzed on edge. Then, the analysis of this data is transmitted over the network.

Athul Uday on Rethinking Marketing Frameworks

This means that we are ideally disregarding some of the raw, unanalyzed data, and potentially missing out on some insights. Again, an important question arises – how bearable is this “loss” of data? Does the organisation need the whole data or is the result generated enough for them? Will missing out on some data negatively affect the organisation’s analysis?

Neural Networks: Applications in the Real World

 

There’s no correct answer to these questions. An aeroplane system can’t afford to miss any data, even a bit of it (no pun intended), so, all of the data should be transferred and analyzed to detect trends and patterns. But, transferring data during flight time is not a good idea. So, a better approach will be to collect the data offline and perform edge computing during the flight time. All in all, edge computing is not a panacea in the world of Information Technology. It is a relatively newer technology that offers a host of benefits. However, it’s still important to know if it fits your organisation’s needs or not.

The bottom line is that data is valuable. All data that can be analyzed should be analyzed to detect patterns and gain insights. In today’s world, data-driven companies are making a lot more progress compared to the traditional ones. Edge Analytics is a new and exciting space and is an answer for maintenance and usability of data, and we can expect to see many more exciting applications of the same in the years to come.

Facebook Comments

Lead the AI Driven Technological Revolution

PG Diploma in Machine Learning and Artificial Intelligence
Learn More